It’s become normal so quickly that many organisations are still catching their breath. Whether people like the change or not, LLMs are here to stay and will continue to shape the way that operations run and businesses define productivity in the months and years to come.
The benefits are obvious enough. Faster drafting, clearer summaries, fresh ideas when everyone is tired. That part feels great. But the shift has been so quick that many businesses are trying to figure out the rules while everyone is already using the tools.
When risks show up in slow, slightly messy ways
A lot of the risks don’t feel dramatic at first. They show up in little moments. Someone pastes a chunk of a private document into a public AI tool just to tidy up the wording. Another person uses their personal account because it feels quicker.
None of this is done with bad intent but later, when someone asks where that information ended up, there’s usually a pause. People assume the system takes care of itself, when in reality no one is quite sure.
Traditional risk frameworks weren’t designed with this kind of behaviour in mind. They’re being updated, but it’s a slower process than the pace at which AI is being adopted. In some organisations, teams think someone else must be handling the governance.
Accuracy is another thing people only notice after it has already caused a headache. LLMs can sound incredibly sure of themselves, but sometimes they mix facts together or create details that aren’t real.
None of this means companies should avoid AI. It just means that publicly available tools present their own challenges, introduce new risks and require serious consideration in terms of company processes and procedures.
How Copilot Enterprise can bring a bit more order to the chaos
Using AI inside a closed environment feels different. Copilot Enterprise works within Microsoft 365, which already holds the critical data and information owned by an organisation. Importantly, it also ensures that this data is not only used to inform the LLM’s output but ensures that this information remains within the company environment.
It doesn’t stop there though. Anyone can sell a licence and provide Copilot Enterprise, but it’s important that its use is enforced over other options that are less secure. Users may be familiar with the free version of Chat GPT, for example, but part of the process of implementing this tool is to educate team members on the importance of using a single, secure solution.
Implementing Copilot enterprise into an environment doesn’t ensure its use. Having the correct controls in place is essential. This extends to ensuring that specific data is redacted from Copilot.
Two main reasons for this spring to mind:
1, Even though a given user may have access to specific data, it doesn’t mean that the organisation approves its inclusion in any AI produced output – especially if such detail is likely to be customer facing.
2, It’s not uncommon for users to be accidentally granted access to data they shouldn’t have and are probably not aware of.
Here’s a working example . . .
Sue has been with the company for years and has held multiple roles during her tenure. She left the organisation having worked in accounts. Jane now joins the company to backfill Sue’s role and her permissions are copied from Sue.
The trouble is, Sue used to be the PA to the Chief Executive and she was left with access to all of the CEO’s personal files. Jane asks Copilot a question with access to the CEO’s dataset. This in turn provides company confidential information inappropriately.
This is the kind of scenario where employing the appropriate experts for AI implementation bears fruit.
What does this mean?
Well, implemented properly AI can be very powerful while ensuring sensitive business financial information won’t find itself floating around the internet at large and user access to data has appropriate controls. Similarly it prevents legal detail or contractual stipulations from falling into the wrong hands.
Copilot also sticks to existing access rules. If a junior team member doesn’t have permission to open a file that’s intended only for senior management then Copilot won’t use this data in response to a prompt from that member of staff. It doesn’t try to be clever by pulling information from places it shouldn’t. That stops many of those awkward “wait, why did it show that?” conversations that tend to happen with open tools.
Copilot Enterprise fits into the controls and security infrastructure that is developed by an organisation rather than adhering to rules that have been determined only by the AI overlords in Silicon Valley or China. It doesn’t create new shadows or unexpected gaps. It just gives people a safer way to use AI without needing to rewrite how the entire organisation works.
It’s not perfect, of course. No LLM or AI tool can yet claim to be invulnerable to mistakes. However, Copilot Enterprise should be considered a valuable tool in any business armoury as organisations look to manage and mitigate the risks associated with data loss or Ai biases.
A steadier way forward
LLMs will keep influencing how teams work, and that shift won’t slow down now. The real challenge is using them in a way that feels sensible rather than rushed.
A structured environment like Copilot Enterprise gives organisations the breathing room they need. It keeps things grounded and avoids the uncertainty that comes with public tools.
If you’d like to explore how Copilot Enterprise could fit into your organisation, why not get in touch with us today.
